{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/blind-justice-fairness-with-encrypted","title":"Blind Justice: Fairness with Encrypted Sensitive Attributes","arxiv_id":"1806.03281","date":"2018-06-08","proceeding":"ICML 2018 7","authors":["Niki Kilbertus","Adrià Gascón","Matt J. Kusner","Michael Veale","Krishna P. Gummadi","Adrian Weller"],"abstract":"Recent work has explored how to train machine learning models which do not\ndiscriminate against any subgroup of the population as determined by sensitive\nattributes such as gender or race. To avoid disparate treatment, sensitive\nattributes should not be considered. On the other hand, in order to avoid\ndisparate impact, sensitive attributes must be examined, e.g., in order to\nlearn a fair model, or to check if a given model is fair. We introduce methods\nfrom secure multi-party computation which allow us to avoid both. 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